{"id":"https://openalex.org/W4255136861","doi":"https://doi.org/10.1109/icpr.2004.1334071","title":"Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers","display_name":"Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W4255136861","doi":"https://doi.org/10.1109/icpr.2004.1334071"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2004.1334071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2004.1334071","pdf_url":null,"source":{"id":"https://openalex.org/S4363608750","display_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079107161","display_name":"Hee-Joong Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I24214720","display_name":"Hansung University","ror":"https://ror.org/048m9x696","country_code":"KR","type":"education","lineage":["https://openalex.org/I24214720"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hee-Joong Kang","raw_affiliation_strings":["Division of Computer Engineering, Hansung University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Division of Computer Engineering, Hansung University, Seoul, South Korea","institution_ids":["https://openalex.org/I24214720"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003875781","display_name":"David Doermann","orcid":"https://orcid.org/0000-0003-1639-4561"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Doermann","raw_affiliation_strings":["Institute of Advanced Computer Studies, University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"Institute of Advanced Computer Studies, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079107161"],"corresponding_institution_ids":["https://openalex.org/I24214720"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47606727,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"252","last_page":"255 Vol.1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.7366441488265991},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5753867030143738},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5597927570343018},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5407711267471313},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5241461396217346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5114063620567322},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"Conditional entropy","score":0.5074178576469421},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4824807941913605},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.47666552662849426},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.470977783203125},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.45671194791793823},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.44629958271980286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44530102610588074},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4440966844558716},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4316602349281311},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4270499348640442},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4259764552116394},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.41832348704338074},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4118618965148926},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.258433073759079},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24390649795532227},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08026084303855896}],"concepts":[{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.7366441488265991},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5753867030143738},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5597927570343018},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5407711267471313},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5241461396217346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5114063620567322},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.5074178576469421},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4824807941913605},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.47666552662849426},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.470977783203125},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.45671194791793823},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.44629958271980286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44530102610588074},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4440966844558716},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4316602349281311},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4270499348640442},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4259764552116394},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.41832348704338074},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4118618965148926},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.258433073759079},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24390649795532227},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08026084303855896},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2004.1334071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2004.1334071","pdf_url":null,"source":{"id":"https://openalex.org/S4363608750","display_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6399999856948853,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1964647586","https://openalex.org/W1968720108","https://openalex.org/W2046485094","https://openalex.org/W2061289977","https://openalex.org/W2084812512","https://openalex.org/W2093467640","https://openalex.org/W2126297944","https://openalex.org/W2129000925","https://openalex.org/W2163166770","https://openalex.org/W2164568552","https://openalex.org/W4243673078","https://openalex.org/W6673992262"],"related_works":["https://openalex.org/W1973600295","https://openalex.org/W1599512561","https://openalex.org/W3143280635","https://openalex.org/W4287244523","https://openalex.org/W28083579","https://openalex.org/W2188630996","https://openalex.org/W2093467640","https://openalex.org/W2340310921","https://openalex.org/W2072663648","https://openalex.org/W2131865294"],"abstract_inverted_index":{"In":[0,22],"combining":[1,78],"multiple":[2],"classifiers":[3,80],"using":[4],"a":[5,8,16,45],"Bayesian":[6],"formalism,":[7],"high":[9],"dimensional":[10],"probability":[11,30],"distribution":[12],"is":[13,57,75],"composed":[14],"of":[15,20,28,35,44],"class":[17,46,69],"and":[18,47,70],"decisions":[19],"classifiers.":[21],"order":[23],"to":[24],"do":[25],"product":[26,55],"approximation":[27,56],"the":[29,32,41,64,68,79],"distribution,":[31],"upper":[33],"bound":[34],"Bayes":[36],"error":[37],"rate,":[38],"bounded":[39],"by":[40,62,77],"conditional":[42],"entropy":[43],"decisions,":[48],"should":[49],"be":[50],"minimized.":[51],"A":[52],"second-order":[53,65],"dependency-based":[54],"proposed":[58,73],"in":[59],"this":[60],"paper":[61],"considering":[63],"dependency":[66],"between":[67],"decisions.":[71],"The":[72],"method":[74],"evaluated":[76],"recognizing":[81],"unconstrained":[82],"handwritten":[83],"numerals.":[84]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}